Alpha Diversity Analysis of Microbiota Dysbiosis in Normal and Colorectal Cancer of Mice Feces
Bibliographic record
Abstract
Background: Colorectal cancer development is influenced by both environmental and genetic factors, with the gut microbiota playing a significant role. This research investigates how alterations in gut microbiota are associated with the incidence, progression, prognosis, and early detection of CRC. Methods: An experimental laboratory study was carried out using Sprague Dawley rats that were induced with azoxymethane (AOM) and Dextran Sodium Sulfate (DSS). The thirty rats were divided into three groups: normal, cancer-induced, and treatment. The fecal microbiota profiles were examined through Next Generation Sequencing (NGS), and the data were analyzed for alpha diversity, highlighting the dynamics of the microbial community. Results: The cancer-induced group (K2 Plus) exhibited the highest microbial diversity across Shannon, Simpson, Chao1, and PD Whole Tree indices, while the treatment group (P2 Plus) demonstrated the lowest. Conclusion: These findings suggest that the increase in diversity observed in cancer-induced mice reflects disruption of community stability and blooming of pathobionts. Conversely, treatment with Lactococcus lactis D4 reduced diversity, potentially by selectively suppressing pro-inflammatory or pathogenic taxa, indicating a beneficial probiotic effect in mitigating dysbiosis associated with colorectal cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".